ParkinsonsDisease_BusinessProject_ML¶

Importing the All neccesary Libraries¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report 
import os

Data set¶

In [2]:
df = pd.read_csv('Parkinsons.csv')
df.sample(5)
Out[2]:
name MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer ... Shimmer:DDA NHR HNR status RPDE DFA spread1 spread2 D2 PPE
30 phon_R01_S07_1 197.076 206.896 192.055 0.00289 0.00001 0.00166 0.00168 0.00498 0.01098 ... 0.01689 0.00339 26.775 0 0.422229 0.741367 -7.348300 0.177551 1.743867 0.085569
86 phon_R01_S21_3 178.222 202.450 141.047 0.00321 0.00002 0.00163 0.00194 0.00488 0.03759 ... 0.06219 0.03151 15.924 1 0.598714 0.712199 -6.366916 0.335753 2.654271 0.144614
153 phon_R01_S37_1 121.345 139.644 98.250 0.00684 0.00006 0.00388 0.00332 0.01164 0.02534 ... 0.04019 0.04179 21.520 1 0.566867 0.670475 -4.865194 0.246404 2.013530 0.168581
80 phon_R01_S20_3 96.106 108.664 84.510 0.00694 0.00007 0.00389 0.00415 0.01168 0.04024 ... 0.06799 0.01823 19.055 1 0.544805 0.770466 -4.441519 0.155097 2.645959 0.327978
113 phon_R01_S26_5 210.141 232.706 185.258 0.00534 0.00003 0.00321 0.00280 0.00964 0.01680 ... 0.02583 0.00620 23.671 1 0.441097 0.722254 -5.963040 0.250283 2.489191 0.177807

5 rows × 24 columns

In [3]:
df.shape
Out[3]:
(195, 24)
In [4]:
len(df)
Out[4]:
195
In [5]:
df.dtypes
Out[5]:
name                 object
MDVP:Fo(Hz)         float64
MDVP:Fhi(Hz)        float64
MDVP:Flo(Hz)        float64
MDVP:Jitter(%)      float64
MDVP:Jitter(Abs)    float64
MDVP:RAP            float64
MDVP:PPQ            float64
Jitter:DDP          float64
MDVP:Shimmer        float64
MDVP:Shimmer(dB)    float64
Shimmer:APQ3        float64
Shimmer:APQ5        float64
MDVP:APQ            float64
Shimmer:DDA         float64
NHR                 float64
HNR                 float64
status                int64
RPDE                float64
DFA                 float64
spread1             float64
spread2             float64
D2                  float64
PPE                 float64
dtype: object
In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB
In [7]:
df.isnull().sum()
Out[7]:
name                0
MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64
In [8]:
df.columns
Out[8]:
Index(['name', 'MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',
       'spread1', 'spread2', 'D2', 'PPE'],
      dtype='object')
In [9]:
df.isna().sum()
Out[9]:
name                0
MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64
In [10]:
df.columns.value_counts()
Out[10]:
name                1
MDVP:Fo(Hz)         1
D2                  1
spread2             1
spread1             1
DFA                 1
RPDE                1
status              1
HNR                 1
NHR                 1
Shimmer:DDA         1
MDVP:APQ            1
Shimmer:APQ5        1
Shimmer:APQ3        1
MDVP:Shimmer(dB)    1
MDVP:Shimmer        1
Jitter:DDP          1
MDVP:PPQ            1
MDVP:RAP            1
MDVP:Jitter(Abs)    1
MDVP:Jitter(%)      1
MDVP:Flo(Hz)        1
MDVP:Fhi(Hz)        1
PPE                 1
dtype: int64
In [123]:
from ydata_profiling import ProfileReport
In [124]:
ProfileReport(df, title="Parkinson's Disease Profile Report")

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[124]:
























In [12]:



     


df['name'].nunique()



     






















    
    
Out[12]:








195




























Target Data Column -- status¶


















In [13]:



     


df['status']



     






















    
    
Out[13]:








0      1
1      1
2      1
3      1
4      1
      ..
190    0
191    0
192    0
193    0
194    0
Name: status, Length: 195, dtype: int64
























In [14]:



     


df['status'].value_counts()



     






















    
    
Out[14]:








1    147
0     48
Name: status, dtype: int64
























In [15]:



     


sns.histplot(df['status'])
plt.show()



     






















    
    































In [16]:



     


plt.figure(figsize=(10, 4))
sns.histplot(df['status'])
plt.xlabel('Status')
plt.ylabel('Frequencies')
ax=plt.gca()
ax.set_facecolor('black')
plt.grid()
plt.show()



     






















    
    































In [17]:



     


plt.figure(figsize=(10, 4))
sns.kdeplot(df['NHR'])
ax=plt.gca()
ax.set_facecolor('black')
plt.grid(axis='y')
plt.show()



     






















    
    































In [18]:



     


plt.figure(figsize=(10, 4))
sns.barplot(x="status",y="NHR",data=df,errorbar=('ci', 95))
plt.grid()
ax=plt.gca()
ax.set_facecolor('black')
plt.show()



     






















    
    































In [19]:



     


plt.figure(figsize=(10, 4))
sns.kdeplot(df['HNR'])
ax=plt.gca()
ax.set_facecolor('black')
plt.grid(axis='y')
plt.show()



     






















    
    































In [20]:



     


plt.figure(figsize=(10,4))
sns.barplot(x="status",y="HNR",data=df)
ax=plt.gca()
ax.set_facecolor('black')
plt.grid()
plt.show()



     






















    
    































In [21]:



     


plt.figure(figsize=(10,4))
sns.kdeplot(df['RPDE'])
ax=plt.gca()
ax.set_facecolor('black')
plt.grid(axis='y')
plt.show()



     






















    
    































In [27]:



     


plt.figure(figsize=(10,4))
sns.barplot(x="status",y="RPDE",data=df)
ax=plt.gca()
ax.set_facecolor('black')
plt.grid(axis='y')
plt.show()



     






















    
    































In [28]:



     


import warnings
warnings.filterwarnings('ignore')
rows=3
cols=7
fig, ax=plt.subplots(nrows=rows,ncols=cols,figsize=(16,6))
col=df.columns
index=1
for i in range(rows):
    for j in range(cols):
        sns.distplot(df[col[index]],ax=ax[i][j])
        index=index+1
        
# plt.tight_layout()
plt.show()



     






















    
    































In [29]:



     


corr = df.corr()
corr



     






















    
    
Out[29]:











  
    

      
      MDVP:Fo(Hz)
      MDVP:Fhi(Hz)
      MDVP:Flo(Hz)
      MDVP:Jitter(%)
      MDVP:Jitter(Abs)
      MDVP:RAP
      MDVP:PPQ
      Jitter:DDP
      MDVP:Shimmer
      MDVP:Shimmer(dB)
      ...
      Shimmer:DDA
      NHR
      HNR
      status
      RPDE
      DFA
      spread1
      spread2
      D2
      PPE
    

  
  
    

      MDVP:Fo(Hz)
      1.000000
      0.400985
      0.596546
      -0.118003
      -0.382027
      -0.076194
      -0.112165
      -0.076213
      -0.098374
      -0.073742
      ...
      -0.094732
      -0.021981
      0.059144
      -0.383535
      -0.383894
      -0.446013
      -0.413738
      -0.249450
      0.177980
      -0.372356
    

    

      MDVP:Fhi(Hz)
      0.400985
      1.000000
      0.084951
      0.102086
      -0.029198
      0.097177
      0.091126
      0.097150
      0.002281
      0.043465
      ...
      -0.003733
      0.163766
      -0.024893
      -0.166136
      -0.112404
      -0.343097
      -0.076658
      -0.002954
      0.176323
      -0.069543
    

    

      MDVP:Flo(Hz)
      0.596546
      0.084951
      1.000000
      -0.139919
      -0.277815
      -0.100519
      -0.095828
      -0.100488
      -0.144543
      -0.119089
      ...
      -0.150737
      -0.108670
      0.210851
      -0.380200
      -0.400143
      -0.050406
      -0.394857
      -0.243829
      -0.100629
      -0.340071
    

    

      MDVP:Jitter(%)
      -0.118003
      0.102086
      -0.139919
      1.000000
      0.935714
      0.990276
      0.974256
      0.990276
      0.769063
      0.804289
      ...
      0.746635
      0.906959
      -0.728165
      0.278220
      0.360673
      0.098572
      0.693577
      0.385123
      0.433434
      0.721543
    

    

      MDVP:Jitter(Abs)
      -0.382027
      -0.029198
      -0.277815
      0.935714
      1.000000
      0.922911
      0.897778
      0.922913
      0.703322
      0.716601
      ...
      0.697170
      0.834972
      -0.656810
      0.338653
      0.441839
      0.175036
      0.735779
      0.388543
      0.310694
      0.748162
    

    

      MDVP:RAP
      -0.076194
      0.097177
      -0.100519
      0.990276
      0.922911
      1.000000
      0.957317
      1.000000
      0.759581
      0.790652
      ...
      0.744919
      0.919521
      -0.721543
      0.266668
      0.342140
      0.064083
      0.648328
      0.324407
      0.426605
      0.670999
    

    

      MDVP:PPQ
      -0.112165
      0.091126
      -0.095828
      0.974256
      0.897778
      0.957317
      1.000000
      0.957319
      0.797826
      0.839239
      ...
      0.763592
      0.844604
      -0.731510
      0.288698
      0.333274
      0.196301
      0.716489
      0.407605
      0.412524
      0.769647
    

    

      Jitter:DDP
      -0.076213
      0.097150
      -0.100488
      0.990276
      0.922913
      1.000000
      0.957319
      1.000000
      0.759555
      0.790621
      ...
      0.744901
      0.919548
      -0.721494
      0.266646
      0.342079
      0.064026
      0.648328
      0.324377
      0.426556
      0.671005
    

    

      MDVP:Shimmer
      -0.098374
      0.002281
      -0.144543
      0.769063
      0.703322
      0.759581
      0.797826
      0.759555
      1.000000
      0.987258
      ...
      0.987626
      0.722194
      -0.835271
      0.367430
      0.447424
      0.159954
      0.654734
      0.452025
      0.507088
      0.693771
    

    

      MDVP:Shimmer(dB)
      -0.073742
      0.043465
      -0.119089
      0.804289
      0.716601
      0.790652
      0.839239
      0.790621
      0.987258
      1.000000
      ...
      0.963202
      0.744477
      -0.827805
      0.350697
      0.410684
      0.165157
      0.652547
      0.454314
      0.512233
      0.695058
    

    

      Shimmer:APQ3
      -0.094717
      -0.003743
      -0.150747
      0.746625
      0.697153
      0.744912
      0.763580
      0.744894
      0.987625
      0.963198
      ...
      1.000000
      0.716207
      -0.827123
      0.347617
      0.435242
      0.151124
      0.610967
      0.402243
      0.467265
      0.645377
    

    

      Shimmer:APQ5
      -0.070682
      -0.009997
      -0.101095
      0.725561
      0.648961
      0.709927
      0.786780
      0.709907
      0.982835
      0.973751
      ...
      0.960072
      0.658080
      -0.813753
      0.351148
      0.399903
      0.213873
      0.646809
      0.457195
      0.502174
      0.702456
    

    

      MDVP:APQ
      -0.077774
      0.004937
      -0.107293
      0.758255
      0.648793
      0.737455
      0.804139
      0.737439
      0.950083
      0.960977
      ...
      0.896647
      0.694019
      -0.800407
      0.364316
      0.451379
      0.157276
      0.673158
      0.502188
      0.536869
      0.721694
    

    

      Shimmer:DDA
      -0.094732
      -0.003733
      -0.150737
      0.746635
      0.697170
      0.744919
      0.763592
      0.744901
      0.987626
      0.963202
      ...
      1.000000
      0.716215
      -0.827130
      0.347608
      0.435237
      0.151132
      0.610971
      0.402223
      0.467261
      0.645389
    

    

      NHR
      -0.021981
      0.163766
      -0.108670
      0.906959
      0.834972
      0.919521
      0.844604
      0.919548
      0.722194
      0.744477
      ...
      0.716215
      1.000000
      -0.714072
      0.189429
      0.370890
      -0.131882
      0.540865
      0.318099
      0.470949
      0.552591
    

    

      HNR
      0.059144
      -0.024893
      0.210851
      -0.728165
      -0.656810
      -0.721543
      -0.731510
      -0.721494
      -0.835271
      -0.827805
      ...
      -0.827130
      -0.714072
      1.000000
      -0.361515
      -0.598736
      -0.008665
      -0.673210
      -0.431564
      -0.601401
      -0.692876
    

    

      status
      -0.383535
      -0.166136
      -0.380200
      0.278220
      0.338653
      0.266668
      0.288698
      0.266646
      0.367430
      0.350697
      ...
      0.347608
      0.189429
      -0.361515
      1.000000
      0.308567
      0.231739
      0.564838
      0.454842
      0.340232
      0.531039
    

    

      RPDE
      -0.383894
      -0.112404
      -0.400143
      0.360673
      0.441839
      0.342140
      0.333274
      0.342079
      0.447424
      0.410684
      ...
      0.435237
      0.370890
      -0.598736
      0.308567
      1.000000
      -0.110950
      0.591117
      0.479905
      0.236931
      0.545886
    

    

      DFA
      -0.446013
      -0.343097
      -0.050406
      0.098572
      0.175036
      0.064083
      0.196301
      0.064026
      0.159954
      0.165157
      ...
      0.151132
      -0.131882
      -0.008665
      0.231739
      -0.110950
      1.000000
      0.195668
      0.166548
      -0.165381
      0.270445
    

    

      spread1
      -0.413738
      -0.076658
      -0.394857
      0.693577
      0.735779
      0.648328
      0.716489
      0.648328
      0.654734
      0.652547
      ...
      0.610971
      0.540865
      -0.673210
      0.564838
      0.591117
      0.195668
      1.000000
      0.652358
      0.495123
      0.962435
    

    

      spread2
      -0.249450
      -0.002954
      -0.243829
      0.385123
      0.388543
      0.324407
      0.407605
      0.324377
      0.452025
      0.454314
      ...
      0.402223
      0.318099
      -0.431564
      0.454842
      0.479905
      0.166548
      0.652358
      1.000000
      0.523532
      0.644711
    

    

      D2
      0.177980
      0.176323
      -0.100629
      0.433434
      0.310694
      0.426605
      0.412524
      0.426556
      0.507088
      0.512233
      ...
      0.467261
      0.470949
      -0.601401
      0.340232
      0.236931
      -0.165381
      0.495123
      0.523532
      1.000000
      0.480585
    

    

      PPE
      -0.372356
      -0.069543
      -0.340071
      0.721543
      0.748162
      0.670999
      0.769647
      0.671005
      0.693771
      0.695058
      ...
      0.645389
      0.552591
      -0.692876
      0.531039
      0.545886
      0.270445
      0.962435
      0.644711
      0.480585
      1.000000
    

  



23 rows × 23 columns


























In [30]:



     


sns.heatmap(corr)
plt.show()



     






















    
    




















































In [31]:



     


plt.figure(figsize=(16,8))
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, cmap='cubehelix',annot = True)
plt.show()



     






















    
    



































Machine Learnig Algorithm¶


















In [32]:



     


df.head()



     






















    
    
Out[32]:











  
    

      
      name
      MDVP:Fo(Hz)
      MDVP:Fhi(Hz)
      MDVP:Flo(Hz)
      MDVP:Jitter(%)
      MDVP:Jitter(Abs)
      MDVP:RAP
      MDVP:PPQ
      Jitter:DDP
      MDVP:Shimmer
      ...
      Shimmer:DDA
      NHR
      HNR
      status
      RPDE
      DFA
      spread1
      spread2
      D2
      PPE
    

  
  
    

      0
      phon_R01_S01_1
      119.992
      157.302
      74.997
      0.00784
      0.00007
      0.00370
      0.00554
      0.01109
      0.04374
      ...
      0.06545
      0.02211
      21.033
      1
      0.414783
      0.815285
      -4.813031
      0.266482
      2.301442
      0.284654
    

    

      1
      phon_R01_S01_2
      122.400
      148.650
      113.819
      0.00968
      0.00008
      0.00465
      0.00696
      0.01394
      0.06134
      ...
      0.09403
      0.01929
      19.085
      1
      0.458359
      0.819521
      -4.075192
      0.335590
      2.486855
      0.368674
    

    

      2
      phon_R01_S01_3
      116.682
      131.111
      111.555
      0.01050
      0.00009
      0.00544
      0.00781
      0.01633
      0.05233
      ...
      0.08270
      0.01309
      20.651
      1
      0.429895
      0.825288
      -4.443179
      0.311173
      2.342259
      0.332634
    

    

      3
      phon_R01_S01_4
      116.676
      137.871
      111.366
      0.00997
      0.00009
      0.00502
      0.00698
      0.01505
      0.05492
      ...
      0.08771
      0.01353
      20.644
      1
      0.434969
      0.819235
      -4.117501
      0.334147
      2.405554
      0.368975
    

    

      4
      phon_R01_S01_5
      116.014
      141.781
      110.655
      0.01284
      0.00011
      0.00655
      0.00908
      0.01966
      0.06425
      ...
      0.10470
      0.01767
      19.649
      1
      0.417356
      0.823484
      -3.747787
      0.234513
      2.332180
      0.410335
    

  



5 rows × 24 columns






























The name column from the data set does not play a significant role for the machine learning analysis. Based on the aforementioned information set for the Parkinson's Diesase test, we must determine the test for both independent and dependent variables. Here, the status will play a dependent variable and the other columns will play for the independent variables to check how the fitness for the played variables for independent lyes for testing the status.



















In [33]:



     


df.drop(columns=['name'], inplace=True)



     























Creating The independent  and dependent columns for testing for machine learning¶


















In [35]:



     


X=df.drop(labels=['status'],axis=1)
X.head()
#Below all are the columns for the independent testing



     






















    
    
Out[35]:











  
    

      
      MDVP:Fo(Hz)
      MDVP:Fhi(Hz)
      MDVP:Flo(Hz)
      MDVP:Jitter(%)
      MDVP:Jitter(Abs)
      MDVP:RAP
      MDVP:PPQ
      Jitter:DDP
      MDVP:Shimmer
      MDVP:Shimmer(dB)
      ...
      MDVP:APQ
      Shimmer:DDA
      NHR
      HNR
      RPDE
      DFA
      spread1
      spread2
      D2
      PPE
    

  
  
    

      0
      119.992
      157.302
      74.997
      0.00784
      0.00007
      0.00370
      0.00554
      0.01109
      0.04374
      0.426
      ...
      0.02971
      0.06545
      0.02211
      21.033
      0.414783
      0.815285
      -4.813031
      0.266482
      2.301442
      0.284654
    

    

      1
      122.400
      148.650
      113.819
      0.00968
      0.00008
      0.00465
      0.00696
      0.01394
      0.06134
      0.626
      ...
      0.04368
      0.09403
      0.01929
      19.085
      0.458359
      0.819521
      -4.075192
      0.335590
      2.486855
      0.368674
    

    

      2
      116.682
      131.111
      111.555
      0.01050
      0.00009
      0.00544
      0.00781
      0.01633
      0.05233
      0.482
      ...
      0.03590
      0.08270
      0.01309
      20.651
      0.429895
      0.825288
      -4.443179
      0.311173
      2.342259
      0.332634
    

    

      3
      116.676
      137.871
      111.366
      0.00997
      0.00009
      0.00502
      0.00698
      0.01505
      0.05492
      0.517
      ...
      0.03772
      0.08771
      0.01353
      20.644
      0.434969
      0.819235
      -4.117501
      0.334147
      2.405554
      0.368975
    

    

      4
      116.014
      141.781
      110.655
      0.01284
      0.00011
      0.00655
      0.00908
      0.01966
      0.06425
      0.584
      ...
      0.04465
      0.10470
      0.01767
      19.649
      0.417356
      0.823484
      -3.747787
      0.234513
      2.332180
      0.410335
    

  



5 rows × 22 columns


























In [36]:



     


Y=df['status']
Y.head()
#Below all are the columns for the dependent for analysing for testing



     






















    
    
Out[36]:








0    1
1    1
2    1
3    1
4    1
Name: status, dtype: int64
























In [37]:



     


Y.value_counts()



     






















    
    
Out[37]:








1    147
0     48
Name: status, dtype: int64
























In [38]:



     


print (X.shape,Y.shape)



     






















    
    






(195, 22) (195,)




























Training and Testing¶


















In [39]:



     


X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,random_state=40)



     



















In [40]:



     


print(X_train.shape)
print(X_test.shape)



     






















    
    






(156, 22)
(39, 22)
























In [41]:



     


print(Y_train.shape)
print(Y_test.shape)



     






















    
    






(156,)
(39,)




























Checking the Data with the Logistic Regression¶


















In [42]:



     


log_reg = LogisticRegression()
log_reg



     






















    
    
Out[42]:







LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
























In [43]:



     


log_reg.fit(X_train, Y_train)



     






















    
    
Out[43]:







LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()




























Prediction on train¶


















In [44]:



     


Y_train[:10]



     






















    
    
Out[44]:








147    1
86     1
179    1
69     1
125    1
42     0
77     1
62     0
153    1
59     1
Name: status, dtype: int64
























In [45]:



     


train_preds = log_reg.predict(X_train)
train_preds



     






















    
    
Out[45]:








array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1], dtype=int64)
























In [46]:



     


print("Model accuracy on train is: ", accuracy_score(Y_train,train_preds))



     






















    
    






Model accuracy on train is:  0.8717948717948718
























In [47]:



     


test_preds = log_reg.predict(X_test)
test_preds



     






















    
    
Out[47]:








array([1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)
























In [48]:



     


Y_test



     






















    
    
Out[48]:








96     1
5      1
116    1
35     0
178    1
185    0
54     1
134    1
90     1
187    0
139    1
142    1
175    0
26     1
89     1
140    1
155    1
23     1
132    1
37     1
151    1
28     1
85     1
93     1
172    0
75     1
18     1
105    1
121    1
130    1
33     0
46     0
166    0
163    1
11     1
164    1
81     1
111    1
67     1
Name: status, dtype: int64
























In [49]:



     


print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds))
print('+'*50)



     






















    
    






Model accuracy on test is:  0.8461538461538461
++++++++++++++++++++++++++++++++++++++++++++++++++
























In [50]:



     


print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds))



     






















    
    






confusion_matrix train is:
  [[ 24  16]
 [  4 112]]
























In [51]:



     


print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds))



     






















    
    






confusion_matrix test is:
  [[ 5  3]
 [ 3 28]]
























In [52]:



     


print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds))



     






















    
    






Classification Report Train is 
              precision    recall  f1-score   support

           0       0.86      0.60      0.71        40
           1       0.88      0.97      0.92       116

    accuracy                           0.87       156
   macro avg       0.87      0.78      0.81       156
weighted avg       0.87      0.87      0.86       156

























In [53]:



     


print('\nClassification Report Train is ')
print(classification_report (Y_test, test_preds))



     






















    
    






Classification Report Train is 
              precision    recall  f1-score   support

           0       0.62      0.62      0.62         8
           1       0.90      0.90      0.90        31

    accuracy                           0.85        39
   macro avg       0.76      0.76      0.76        39
weighted avg       0.85      0.85      0.85        39





























Checking for the Random Forest Model¶


















In [54]:



     


Rf = RandomForestClassifier()
Rf



     






















    
    
Out[54]:







RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier()
























In [55]:



     


Rf.fit(X_train,Y_train)



     






















    
    
Out[55]:







RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier()
























In [56]:



     


train_predsRf = Rf.predict(X_train)
train_predsRf



     






















    
    
Out[56]:








array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0,
       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
       1, 1], dtype=int64)
























In [57]:



     


print("Model accuracy on train is:",accuracy_score(Y_train, train_predsRf))



     






















    
    






Model accuracy on train is: 1.0
























In [58]:



     


test_predsRf  = Rf.predict(X_test)
test_predsRf



     






















    
    
Out[58]:








array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)
























In [59]:



     


print("Model accuracy on train is:",accuracy_score(Y_test,test_predsRf))



     






















    
    






Model accuracy on train is: 0.8974358974358975
























In [60]:



     


print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_predsRf))



     






















    
    






confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
























In [61]:



     


print("confusion_matrix train is:\n ", confusion_matrix(Y_test, test_predsRf))



     






















    
    






confusion_matrix train is:
  [[ 6  2]
 [ 2 29]]
























In [62]:



     


print('\nClassification Report Train is ')
print(classification_report (Y_train, train_predsRf))



     






















    
    






Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156

























In [63]:



     


print('\nClassification Report Train is ')
print(classification_report (Y_test, test_predsRf))



     






















    
    






Classification Report Train is 
              precision    recall  f1-score   support

           0       0.75      0.75      0.75         8
           1       0.94      0.94      0.94        31

    accuracy                           0.90        39
   macro avg       0.84      0.84      0.84        39
weighted avg       0.90      0.90      0.90        39

























In [64]:



     


print((Y_test !=test_predsRf).sum(),'/',((Y_test == test_predsRf).sum()+(Y_test != test_predsRf).sum()))



     






















    
    






4 / 39
























In [65]:



     


print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_predsRf))



     






















    
    






KappaScore is:  0.685483870967742




























Final Result we got from Randomforest Classifier¶
Below is the Data Frame for expected and the test Results¶


















In [66]:



     


ddf=pd.DataFrame(data=[test_predsRf,Y_test])
display (ddf)



     






















    
    











  
    

      
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      ...
      29
      30
      31
      32
      33
      34
      35
      36
      37
      38
    

  
  
    

      0
      1
      1
      1
      0
      1
      1
      1
      1
      1
      1
      ...
      1
      0
      0
      0
      1
      1
      1
      1
      0
      1
    

    

      1
      1
      1
      1
      0
      1
      0
      1
      1
      1
      0
      ...
      1
      0
      0
      0
      1
      1
      1
      1
      1
      1
    

  



2 rows × 39 columns


























In [67]:



     


ddf.T



     






















    
    
Out[67]:











  
    

      
      0
      1
    

  
  
    

      0
      1
      1
    

    

      1
      1
      1
    

    

      2
      1
      1
    

    

      3
      0
      0
    

    

      4
      1
      1
    

    

      5
      1
      0
    

    

      6
      1
      1
    

    

      7
      1
      1
    

    

      8
      1
      1
    

    

      9
      1
      0
    

    

      10
      1
      1
    

    

      11
      1
      1
    

    

      12
      0
      0
    

    

      13
      1
      1
    

    

      14
      1
      1
    

    

      15
      1
      1
    

    

      16
      1
      1
    

    

      17
      1
      1
    

    

      18
      1
      1
    

    

      19
      1
      1
    

    

      20
      1
      1
    

    

      21
      0
      1
    

    

      22
      1
      1
    

    

      23
      1
      1
    

    

      24
      0
      0
    

    

      25
      1
      1
    

    

      26
      1
      1
    

    

      27
      1
      1
    

    

      28
      1
      1
    

    

      29
      1
      1
    

    

      30
      0
      0
    

    

      31
      0
      0
    

    

      32
      0
      0
    

    

      33
      1
      1
    

    

      34
      1
      1
    

    

      35
      1
      1
    

    

      36
      1
      1
    

    

      37
      0
      1
    

    

      38
      1
      1
    

  































Analysing for Decision Tree classifier¶


















In [68]:



     


from sklearn.tree import DecisionTreeClassifier



     



















In [69]:



     


DT = DecisionTreeClassifier()
DT



     






















    
    
Out[69]:







DecisionTreeClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()
























In [70]:



     


DT.fit(X_train,Y_train)



     






















    
    
Out[70]:







DecisionTreeClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()




























Training the Models`¶


















In [71]:



     


train_predsDT=DT.predict(X_train)
train_predsDT



     






















    
    
Out[71]:








array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0,
       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
       1, 1], dtype=int64)
























In [72]:



     


print("Model accuracy on train is:",accuracy_score(Y_train,train_predsDT))
print('+'*50)



     






















    
    






Model accuracy on train is: 1.0
++++++++++++++++++++++++++++++++++++++++++++++++++




























Testing the Models¶


















In [73]:



     


test_predsDT = DT.predict(X_test)
test_predsDT



     






















    
    
Out[73]:








array([1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,
       1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)
























In [74]:



     


print("Model accuracy on train is:",accuracy_score(Y_test,test_predsDT))
print('+'*50)



     






















    
    






Model accuracy on train is: 0.9230769230769231
++++++++++++++++++++++++++++++++++++++++++++++++++
























In [75]:



     


print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_predsDT))



     






















    
    






confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
























In [76]:



     


print("confusion_matrix test is: \n", confusion_matrix(Y_test, test_predsDT))



     






















    
    






confusion_matrix test is: 
 [[ 8  0]
 [ 3 28]]
























In [77]:



     


print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')



     






















    
    






Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
























In [78]:



     


print(classification_report (Y_train, train_predsDT))



     






















    
    






              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156

























In [79]:



     


print('\nClassification Report Test is ')
print(classification_report (Y_test, test_predsDT))



     






















    
    






Classification Report Test is 
              precision    recall  f1-score   support

           0       0.73      1.00      0.84         8
           1       1.00      0.90      0.95        31

    accuracy                           0.92        39
   macro avg       0.86      0.95      0.90        39
weighted avg       0.94      0.92      0.93        39

























In [80]:



     


print((Y_test !=test_predsDT).sum(),'/',((Y_test == test_predsDT).sum()+(Y_test != test_predsDT).sum()))
print('+'*50)



     






















    
    






3 / 39
++++++++++++++++++++++++++++++++++++++++++++++++++




























Kappa Score¶


















In [81]:



     


print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_predsDT))



     






















    
    






KappaScore is:  0.7929203539823009




























Analysing using Naive Bayce algorithm¶


















In [82]:



     


from sklearn.naive_bayes import GaussianNB



     



















In [83]:



     


NB = GaussianNB()
NB



     






















    
    
Out[83]:







GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GaussianNB()




























Training the Models`¶


















In [84]:



     


NB.fit(X_train, Y_train)



     






















    
    
Out[84]:







GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GaussianNB()
























In [85]:



     


train_predsNB = NB.predict(X_train)
train_predsNB



     






















    
    
Out[85]:








array([1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,
       1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,
       1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,
       1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
       0, 1], dtype=int64)
























In [86]:



     


print("Model accuracy on train is:",accuracy_score(Y_train,train_predsNB))
print('+'*50)



     






















    
    






Model accuracy on train is: 0.7307692307692307
++++++++++++++++++++++++++++++++++++++++++++++++++




























Testing the Models¶


















In [87]:



     


test_predsNB = NB.predict(X_test)
test_predsNB



     






















    
    
Out[87]:








array([1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,
       1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1], dtype=int64)
























In [88]:



     


print("Model accuracy on train is:",accuracy_score(Y_test,test_predsNB))
print('+'*50)



     






















    
    






Model accuracy on train is: 0.6923076923076923
++++++++++++++++++++++++++++++++++++++++++++++++++




























Confusion matrix¶


















In [89]:



     


print("confusion_matrix train is: \n", confusion_matrix(Y_train, train_predsNB))



     






















    
    






confusion_matrix train is: 
 [[38  2]
 [40 76]]
























In [90]:



     


print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_predsNB))



     






















    
    






confusion_matrix test is:
  [[ 8  0]
 [12 19]]
























In [91]:



     


print('Wrong predictions out of total')
print('+'*50)



     






















    
    






Wrong predictions out of total
++++++++++++++++++++++++++++++++++++++++++++++++++
























In [92]:



     


print('\nClassification Report Train is \n')
print(classification_report (Y_train, train_predsNB))



     






















    
    






Classification Report Train is 

              precision    recall  f1-score   support

           0       0.49      0.95      0.64        40
           1       0.97      0.66      0.78       116

    accuracy                           0.73       156
   macro avg       0.73      0.80      0.71       156
weighted avg       0.85      0.73      0.75       156

























In [93]:



     


print('\nClassification Report Test is ')
print(classification_report (Y_test, test_predsNB))



     






















    
    






Classification Report Test is 
              precision    recall  f1-score   support

           0       0.40      1.00      0.57         8
           1       1.00      0.61      0.76        31

    accuracy                           0.69        39
   macro avg       0.70      0.81      0.67        39
weighted avg       0.88      0.69      0.72        39

























In [94]:



     


print((Y_test !=test_predsNB).sum(),'/',((Y_test == test_predsNB).sum()+(Y_test != test_predsNB).sum()))
print('+'*10)



     






















    
    






12 / 39
++++++++++




























Kappa Score¶


















In [95]:



     


print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_predsNB))



     






















    
    






KappaScore is:  0.3937823834196892




























Analysing the Models using K Neighbours Classifier¶


















In [96]:



     


from sklearn.neighbors import KNeighborsClassifier



     



















In [97]:



     


KNN = KNeighborsClassifier()
KNN



     






















    
    
Out[97]:







KNeighborsClassifier()
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KNeighborsClassifier()




























Training the Data Models¶


















In [98]:



     


KNN.fit(X_train,Y_train)



     






















    
    
Out[98]:







KNeighborsClassifier()
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KNeighborsClassifier()




























Predicting Train Models¶


















In [99]:



     


train_predsKNN = KNN.predict(X_train)
train_predsKNN



     






















    
    
Out[99]:








array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0,
       1, 1], dtype=int64)
























In [100]:



     


print("Model accuracy on train is: ", accuracy_score(Y_train, train_predsKNN))
print("+"*50)



     






















    
    






Model accuracy on train is:  0.9102564102564102
++++++++++++++++++++++++++++++++++++++++++++++++++




























Predicting Test Models¶


















In [101]:



     


test_predsKNN = KNN.predict(X_test)
test_predsKNN



     






















    
    
Out[101]:








array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)
























In [102]:



     


Y_test



     






















    
    
Out[102]:








96     1
5      1
116    1
35     0
178    1
185    0
54     1
134    1
90     1
187    0
139    1
142    1
175    0
26     1
89     1
140    1
155    1
23     1
132    1
37     1
151    1
28     1
85     1
93     1
172    0
75     1
18     1
105    1
121    1
130    1
33     0
46     0
166    0
163    1
11     1
164    1
81     1
111    1
67     1
Name: status, dtype: int64
























In [103]:



     


print("Model accuracy on train is: ", accuracy_score(Y_test,test_preds))
print("+"*50)



     






















    
    






Model accuracy on train is:  0.8461538461538461
++++++++++++++++++++++++++++++++++++++++++++++++++




























confusion Matrix¶


















In [104]:



     


print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_predsKNN))



     






















    
    






confusion_matrix train is:
  [[ 30  10]
 [  4 112]]
























In [105]:



     


print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_predsKNN))



     






















    
    






confusion_matrix test is:
  [[ 4  4]
 [ 2 29]]
























In [106]:



     


print('Wrong predictions out of total')
print('+'*50)
print('\nClassification Report Train is ')



     






















    
    






Wrong predictions out of total
++++++++++++++++++++++++++++++++++++++++++++++++++

Classification Report Train is 
























In [107]:



     


print(classification_report (Y_train, train_predsKNN))



     






















    
    






              precision    recall  f1-score   support

           0       0.88      0.75      0.81        40
           1       0.92      0.97      0.94       116

    accuracy                           0.91       156
   macro avg       0.90      0.86      0.88       156
weighted avg       0.91      0.91      0.91       156

























In [108]:



     


print('\nClassification Report Test is \n')
print(classification_report (Y_test, test_predsKNN))



     






















    
    






Classification Report Test is 

              precision    recall  f1-score   support

           0       0.67      0.50      0.57         8
           1       0.88      0.94      0.91        31

    accuracy                           0.85        39
   macro avg       0.77      0.72      0.74        39
weighted avg       0.84      0.85      0.84        39

























In [109]:



     


print((Y_test !=test_predsKNN).sum(),'/',((Y_test == test_predsKNN).sum()+(Y_test != test_predsKNN).sum()))
print("+"*20)
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_predsKNN))



     






















    
    






6 / 39
++++++++++++++++++++
KappaScore is:  0.48




























Analysing from Support Vector Machine Classifier¶


















In [110]:



     


from sklearn.svm import SVC



     



















In [111]:



     


SVM = SVC(kernel='linear')
SVM



     






















    
    
Out[111]:







SVC(kernel='linear')
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SVC(kernel='linear')




























Training Data Set models¶


















In [112]:



     


SVM.fit(X_train, Y_train)



     






















    
    
Out[112]:







SVC(kernel='linear')
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SVC(kernel='linear')
























In [113]:



     


train_predsSVM = SVM.predict(X_train)
train_predsSVM



     






















    
    
Out[113]:








array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1], dtype=int64)
























In [114]:



     


print("Model accuracy on train is: ", accuracy_score(Y_train, train_predsSVM))
print("+"*50)



     






















    
    






Model accuracy on train is:  0.8782051282051282
++++++++++++++++++++++++++++++++++++++++++++++++++




























Testing the models¶


















In [115]:



     


test_predsSMV = SVM.predict(X_test)
test_predsSMV



     






















    
    
Out[115]:








array([1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1], dtype=int64)
























In [116]:



     


print("Model accuracy on train is: ", accuracy_score(Y_test,test_predsSMV))
print("+"*50)



     






















    
    






Model accuracy on train is:  0.8974358974358975
++++++++++++++++++++++++++++++++++++++++++++++++++




























Confusion matrix¶


















In [117]:



     


print("Confusion matrix for the Training Data models: \n" ,confusion_matrix(Y_train,train_predsSVM))



     






















    
    






Confusion matrix for the Training Data models: 
 [[ 23  17]
 [  2 114]]
























In [121]:



     


print("Confusion matrix for the Testing Data models: \n" ,confusion_matrix(Y_test,test_predsSMv))



     






















    
    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[121], line 1
----> 1 print("Confusion matrix for the Testing Data models: \n" ,confusion_matrix(Y_test,test_predsSMv))

NameError: name 'test_predsSMv' is not defined























In [119]:



     


print('Wrong predictions out of total')
print('-'*30)

print("recall", metrics.recall_score(Y_test, test_predsSMV))
print('-'*30)



     






















    
    






Wrong predictions out of total
------------------------------
recall 0.967741935483871
------------------------------
























In [120]:



     


print('\nClassification Report Train is \n')
print(classification_report (Y_train, train_predsSVM))



     






















    
    






Classification Report Train is 

              precision    recall  f1-score   support

           0       0.92      0.57      0.71        40
           1       0.87      0.98      0.92       116

    accuracy                           0.88       156
   macro avg       0.90      0.78      0.82       156
weighted avg       0.88      0.88      0.87       156

























In [ ]:



     


print('\nClassification Report Test is \n')
print(classification_report (Y_test, test_predsSMV))   



     



















In [ ]:



     


print((Y_test !=test_predsSMV).sum(),'/',((Y_test == test_predsSMV).sum()+(Y_test != test_predsSMV).sum()))



     























Kappa Score¶


















In [122]:



     


print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_predsSMV))



     






















    
    






KappaScore is:  0.6533333333333333